Conversational agents have been studied as tools to scaffold planning and self-reflection for productivity and well-being. While prior work has demonstrated positive outcomes, we still lack a clear understanding of what drives these results and how users behave and communicate with agents that act as coaches rather than assistants. Such understanding is critical for designing interactions in which agents foster meaningful behavioral change. We conducted a 14-day longitudinal study with 12 participants using a proactive agent that initiated regular check-ins to support daily planning and reflection. Our findings reveal diverse interaction patterns: participants accepted or negotiated suggestions, developed shared mental models, reported progress, and at times resisted or disengaged. We also identified problematic aspects of the agent’s behavior, including rigidity, premature turn-taking, and overpromising. Our work contributes to understanding how people interact with a proactive, coach-like agent and offers design considerations for facilitating effective behavioral change.

As large language models (LLMs) become widespread, students increasingly turn to systems like ChatGPT for writing tasks. Educators worry that this reliance may reduce critical engagement with writing and hinder students’ learning processes. Although datasets exist on students’ use of LLMs for writing, how they functionally use ChatGPT in detail—and how this usage shapes their writing and perceptions—remains underexplored. We conducted an online study (n=77) in which students wrote an essay using an in-house ChatGPT we developed to capture their queries. Through qualitative analysis, we identified the types of assistance students sought and presented patterns of use, ranging from asking for opinions on a topic to delegating the entire writing task to ChatGPT. We also found that students’ writing self-efficacy influenced their querying patterns and that levels of ownership and creativity varied depending on how they used ChatGPT. This study contributes empirical data to ongoing discussions about how writing education should incorporate or regulate LLM-powered tools.

Early counting forms a critical foundation for numeracy, involving coordination of visual representations, verbal number words, and physical actions such as pointing. Designing effective technologies for young children, therefore requires careful calibration of multimodal features. This study investigated how different levels of demonstrations paired with a voice assistant—static (baseline: image+voice), animated (animation+voice), and interactive (touch+animation+voice)—influence counting-related understanding and engagement in 2–4-year-olds. We developed a tablet-based counting game and conducted a within-subjects study with 32 children. Results showed that animated demonstration improved cardinal number word understanding over both baseline and the interactive demonstration. Analyses of verbal counting engagement showed that concurrent touch demands increased cognitive load, limiting children’s counting aloud. These findings suggest that more interactivity does not always yield better outcomes for young learners. We contribute empirical evidence and design guidance: voice+animation supports early counting, while touch interactivity should be lightweight and age-appropriate, informing the design of multimodal voice-assisted applications.

University research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it requires ongoing maintenance and is challenging to navigate. Drawing on formative interviews that revealed organizational memory challenges in labs, we designed CHOIR, an LLM-based chatbot that supports organizational memory through four key functions: document-grounded Q&A, Q&A sharing for follow-up discussion, knowledge extraction from conversations, and AI-assisted document updates. We deployed CHOIR in four research labs for one month (n=21), where the lab members asked 107 questions and lab directors updated documents 38 times in the organizational memory. Our findings reveal a privacy-awareness tension: questions were asked privately, limiting directors’ visibility into documentation gaps. Students often avoided contribution due to challenges in generalizing personal experiences into universal documentation. We contribute design implications for privacy-preserving awareness and supporting context-specific knowledge documentation.

Autistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may reproduce stereotypes, raising uncertainty about the risks and benefits of disclosure. We conducted a mixed-methods study combining a large-scale LLM audit experiment with interviews involving 11 autistic participants. We developed a six-step pipeline operationalizing 12 documented autism stereotypes into decision-making scenarios framed as users requesting advice (e.g., “Should I do A or B?”). We generated 345,000 responses from six LLMs and measured how advice shifted when prompts disclosed autism versus when they did not. When autism was disclosed, LLMs disproportionately recommended avoiding stereotypically stressful situations, including social events, confrontations, new experiences, and romantic relationships. While some participants viewed this as affirming, others criticized it as infantilizing or undermining opportunities for growth. Our study illuminates how the intermingling of affirmation and stereotyping complicates the personalization of LLMs.1

Non-native English speakers performing English-related tasks at work struggle to sustain EFL learning, despite their motivation. Often, study materials are disconnected from their work context. Our formative study revealed that reviewing work-related English becomes burdensome with current systems, especially after work. Although workers rely on LLM-based assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages these on-the-fly queries using AI to generate personalized quizzes that workers can review and practice on their smartphones. We conducted a three-week deployment study with 28 EFL workers to evaluate LingoQ. Participants valued the quality-assured, work-situated quizzes and constantly engaging with the app during the study. This active engagement improved self-efficacy and led to learning gains for beginners and, potentially, for intermediate learners. Drawing on these results, we discuss design implications for leveraging workers’ growing reliance on LLMs to foster proficiency and engagement while respecting work boundaries and ethics.

Cybergrooming is a form of online abuse that threatens teens’ mental health and physical safety. Yet, most prior work has focused on detecting perpetrators’ behaviors, leaving a limited understanding of how teens might respond to such unwanted advances. To address this gap, we conducted an online survey with 74 participants—51 parents and 23 teens—who responded to simulated cybergrooming scenarios in two ways: responses that they think would make teens more vulnerable or resilient to unwanted sexual advances. Through a mixed-methods analysis, we identified four types of vulnerable responses (encouraging escalation, accepting an advance, displaying vulnerability, and negating risk concern) and four types of protective strategies (setting boundaries, directly declining, signaling risk awareness, and leveraging avoidance techniques). As the cybergrooming risk escalated, both vulnerable responses and protective strategies showed a corresponding progression. This study contributes a teen-centered understanding of cybergrooming, a labeled dataset, and a stage-based taxonomy of perceived protective strategies, while offering implications for educational programs and sociotechnical interventions.

Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70% positive or neutral responses, yielding a 43% relative improvement.

Reaction videos (RVs) are surging in popularity, emerging as a distinctive facet of participatory culture on modern social video-sharing platforms such as YouTube, TikTok, and Twitch. This study aims to explore not only the motivations and engagement patterns of viewers towards RVs, but also the underlying nature of the virality and community-building phenomena that this sub-genre of video content fosters. We conducted 16 semi-structured interviews with individuals who identified as regular viewers of reaction videos to gain a deeper understanding of how they discover reaction videos (RQ1), the values that drive their motivations for viewing RVs (RQ2), and the ways viewers engage with RVs (RQ3). Our research highlights the variety of original content that RVs utilize, ranging from movie trailers to music releases, and the different engagement strategies viewers rely on in their consumption of RVs. Our research highlights the wide range of original content that RVs engage with, from movie trailers to music releases, as well as the diverse engagement strategies viewers employ when consuming RVs. Our findings highlight the significance of emotional connections with reactors, the value of communal experiences centered on shared interests, and the role of RVs in facilitating content discovery and critique. Drawing on viewers’ experiences with RVs, this work offers a behind-the-scenes perspective on the complex dynamics of spectatorship and its influence on the production and consumption of content in today’s digital landscape. We further contribute to a nuanced and comprehensive understanding of reaction video culture.

Some people suggest that deliberately watching the camera during video calls can simulate eye contact and help build trust. In this study, we investigated the effects of simulated eye contact in video calls and job interviews through an experimental study and a survey. Study 1 involved participants in a mock interview as an interviewer, where a confederate interviewee simulated eye contact half the time. The gaze patterns of the participants were tracked to understand the effects. In Study 2, we conducted an online survey to confirm the findings of Study 1 on a larger scale by asking those with experience interviewing to evaluate interviewees based on interview videos, half of which simulated eye contact. The results of both studies indicate that simulated eye contact had little impact on their evaluation compared to common belief. We discuss how the results motivate future work and how computational approaches to correcting eye gaze can be deceptive.

Background and Motivation. Live coding is a common pedagogical technique where instructors write code in real time during lectures. For students, the main drawbacks of live coding are that it can feel too fast and it can be difficult to take notes. Objectives. Our work seeks to improve the student experience in live coding lectures by: (1) understanding how instructors expect students to take notes and what challenges students face in doing so; and (2) investigating whether a specialized note-taking tool can help students keep up with the pace of the lecture and take better notes. Methods. Based on interviews with instructors who use live coding (n=10), we designed a simple note-taking interface consisting of a rich text editor which allows students to take snapshots of the instructor’s code. We conducted a within-subjects lab experiment (n=57) comparing our interface with a traditional code editor during two 15-minute live coding lectures. We used quizzes and surveys to assess learning, mental workload, and student perceptions, and analyzed students’ notes to determine how much information was captured from the lecture. Findings. In the experimental condition, NASA-TLX surveys indicated a significantly lower mentalworkload and students reported that they could more easily keep up with the lecture. Additionally, students perceived their notes to be more useful and our analysis revealed that the notes had significantly more information from the lecture and provided more context for copied code. Despite these benefits, we did not see a significant difference in learning between the two conditions. Implications. Our results show that during live coding lectures, we can decrease student mental workload and increase the quality of notes by providing an interface which (1) allows capturing the instructor’s code without having to type it out; and (2) maintains a clear visual distinction between code snippets and other text. Future work may examine if such an interface can lead to learning gains over long-term use in the classroom.

This poster presents the development and implementation of a 10-day remix-based summer camp curriculum designed to introduce high school students, particularly a multinational cohort of young women, to programming through creative coding. The curriculum integrates music composition using EarSketch and web development with HTML and CSS. The camp aims to inspire participants to gain self-efficacy in programming and motivate them to explore STEM/computing careers. Preliminary results from surveys and interviews indicate increased confidence in programming skills. This ongoing research explores the impact of remixing as a gateway for transitioning into more general-purpose computing domains such as web development.

One challenge in technical interviews is the thinkaloud process, where candidates verbalize their thought processes while solving coding tasks. Despite its importance, opportunities for structured practice remain limited. Conversational AI offers potential assistance, but limited research explores user perceptions of its role in think-aloud practice. To address this gap, we conducted a study with 17 participants using an LLM-based technical interview practice tool. Participants valued AI’s role in simulation, feedback, and learning from generated examples. Key design recommendations include promoting social presence in conversational AI for technical interview simulation, providing feedback beyond verbal content analysis, and enabling crowdsourced think-aloud examples through humanAI collaboration. Beyond feature design, we examined broader considerations, including intersectional challenges and potential strategies to address them, how AI-driven interview preparation could promote equitable learning in computing careers, and the need to rethink AI’s role in interview practice by suggesting a research direction that integrates human-AI collaboration.

Online hate speech has become increasingly prevalent on social media, causing harm to individuals and society. While automated content moderation has received considerable attention, user-driven counterspeech remains a less explored yet promising approach. However, many people face difficulties in crafting effective responses. We introduce CounterQuill, a human-AI collaborative system that helps everyday users with writing empathetic counterspeech-not by generating automatic replies, but by educating them through reflection and response. CounterQuill follows a three-stage workflow grounded in computational thinking: (1) a learning session to build understanding of hate speech and counterspeech, (2) a brainstorming session to identify harmful patterns and ideate counterspeech ideas, and (3) a co-writing session that helps users refine their counter responses while preserving personal voice. Through a user study ($\mathbf{N} \boldsymbol{=} \mathbf{2 0}$), we found that CounterQuill helped participants develop the skills to brainstorm and draft counterspeech with confidence and control throughout the process. Our findings highlight how AI systems can scaffold complex communication tasks through structured, human-centered workflows that educate users on how to recognize, reflect on, and respond to online hate speech.

Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlight frequently discussed themes and surface underrepresented viewpoints, making accurate representations of insight occurrence essential. Yet authoring presentations in real time is cognitively overwhelming due to the volume of data and tight time constraints. We present Dynamite, an AI-assisted system that enables semantic updates to instructor-authored slides during live classroom discussions. These updates are powered by semantic data binding, which links slide content to evolving discussion data, and semantic suggestions, which offer revision options aligned with pedagogical goals. In a within-subject in-lab study with 12 participants, Dynamite outperformed a text-based AI baseline in content accuracy and quality. Participants used voice and sketch input to quickly organize semantic blocks, then applied suggestions to accelerate refinement as data stabilized.

Workers in outdoor settings encounter numerous constraints, such as weather conditions, safety-related issues, Wi-Fi connectivity, and frequent mobility. These constraints significantly influence their work efficiency, job satisfaction, safety, and well-being. Despite the development of advanced artificial intelligence, there is still much room for improvement in utilizing such technology for workers in outdoor environments. The present study addresses this gap by conducting a systematic review to understand the state of the art regarding the functions, agent types, technologies, evaluation methods, and dependent variables associated with intelligent agents for outdoor workers. The goal of the present study is to provide research directions and guidelines for the development of intelligent agents by addressing research questions, which aim to meet the specific needs of outdoor workers, ultimately contributing to improved working conditions and enhanced safety.

The prevalence of distressing content on social media raises concerns about users' mental well-being, prompting the use of trigger warnings (TW) and content warnings (CW). However, inconsistent presentation of TW/CW across platforms and the lack of standardized practices confuse users regarding these warnings. To better understand how users experienced and utilized these warnings, we conducted a semi-structured interview study with 15 social media users. Our findings reveal challenges across three key stakeholders: viewers, who need to decide whether to engage with warning-labeled content; posters, who struggle with whether and how to apply TW/CW to the content; and platforms, whose design features shape the visibility and usability of warnings. While users generally expressed positive attitudes toward warnings, their understanding of TW/CW usage was limited. Based on these insights, we reflected on the TW/CW mechanisms from multiple stakeholders' perspectives. Lastly, we further reflected on our findings and discussed the opportunities for social media platforms to enhance users' TW/CW experiences, fostering a more trauma-informed social media environment.

Version control systems, which have proven essential for software engineering, can also provide value to creative and artistic practices. In this paper, we explore version control in the creative domain of live coding music, a generative performance practice where programmers edit and run code live to generate audiovisual artifacts. To that end, we developed SHARP, a lightweight version control system that live coders can use during performances as well as in preparation or practice sessions. We conducted a user study where live coders used SHARP for several weeks, wrote diary entries reflecting on their sessions, recorded a performance using SHARP, and participated in exit interviews. We found that SHARP enabled participants to engage with musical form on the fly in novel ways. In addition, the study revealed multifaceted perspectives on how and when versioning can be useful in the context of live coding. Our results inform the design of versioning systems for live coding and more generally for performance and generative arts practices.

Effective self-tracking in working contexts empowers individuals to explore and reflect on past activities. Recordings of computer activities contain rich metadata that can offer valuable insight into users’ previous tasks and endeavors. However, presenting a simple summary of time usage may not effectively engage users with data because it is not contextualized, and users may not understand what to do with the data. This work explores time-lapse videos as a visual-temporal medium to facilitate self-reflection among workers in productivity contexts. To explore this space, we conducted a four-week study (n = 15) to investigate how a computer screen’s history of states can help workers recall previous undertakings and gain comprehensive insights via self-reflection. Our results support that watching time-lapse videos can enhance self-reflection more effectively than traditional self-tracking tools by providing contextual clues about users’ past activities. The experience with both traditional tools and time-lapse videos resulted in increased productivity. Additionally, time-lapse videos assist users in cultivating a positive understanding of their work. We discuss how multimodal cues, such as time-lapse videos, can complement personal informatics tools.

Trigger-Action Programming (TAP) allows endusers to automate IoT devices, social media, and other services. TAP systems typically offer 1) user-friendly, GUI-mediated access to service APIs through “triggers” and “actions” and 2) a simplified if-this-then-that programming model. While simple, TAP’s programming model lacks power and limits what its users can create. We introduce Legato, an automation platform which keeps the convenient trigger and action abstractions from TAP, but allows programmers to connect these triggers and actions with complex logic via JavaScript. Legato piggybacks on IFTTT, taking advantage of IFTTT’s mature ecosystem of integrations. Additionally, Legato allows programmers to store and retrieve persistent state, schedule future events, and safely test their programs. Through a two-stage usability study, we found student programmers easily learned Legato and came up with several scenarios requiring Legato’s power. Based on feedback and observation, we derive design recommendations for future highly-expressive automation systems and reflect on the role of textual programming for end-user automation.

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Research has identified applications of handheld-based VR, which utilizes handheld displays or mobile devices, for developing systems that involve users in mixed reality (MR) without the need for head-worn displays (HWDs). Such systems can potentially accommodate large groups of users participating in MR. However, we lack an understanding of how group sizes and interaction methods affect the user experience. In this paper, we aim to advance our understanding of handheld-based MR in the context of multiplayer, co-located games. We conducted a study (N = 38) to understand how user experiences vary by group size (2, 4, and 8) and interaction method (proximity-based or pointing-based). For our experiment, we implemented a multiuser experience for up to ten users. We found that proximity-based interaction that encouraged dynamic movement positively affected social presence and physical/temporal workload. In bigger group settings, participants felt less challenged and less positive. Individuals had varying preferences for group size and interaction type. The findings of the study will advance our understanding of the design space for handheld-based MR in terms of group sizes and interaction schemes. To make our contributions explicit, we conclude our paper with design implications that can inform user experience design in handheld-based mixed reality contexts.

GIFs afford a high degree of personalization, as they are often created from popular movie and video clips with diverse and realistic characters, each expressing a nuanced emotional state through a combination of characters' own unique bodily gestures and distinctive visual backgrounds. These properties of high personalization and embodiment provide a unique window for exploring how individuals represent and express themselves on social media through the lens of the GIFs they use. In this study, we explore how Twitter users express their gender and racial identities through characters in GIFs. We conducted a behavioral study (n=398) to simulate a series of tweeting and GIF-picking scenarios. We annotated the gender and race identities of GIF characters, and we found that gender and race identities have significant impacts on users' GIF choices: men chose more gender-matching GIFs than women, and White participants chose more race-matching GIFs than Black participants. We also found that users' prior familiarity with the source of a GIF and perceptions about the composition of the audience (viz., having a matching identity) have significant effects on whether a user will choose race- and gender-matching GIFs. This work has implications for practitioners supporting personalized social identity construction and impression management mechanisms online.

Handheld devices have become an inclusive alternative to head-mounted displays in virtual reality (VR) environments, enhancing accessibility and allowing cross-device collaboration. Object manipulation techniques in 3D space with handheld devices, such as those in handheld augmented reality (AR), have been typically evaluated in table-top scale and we currently lack an understanding of how these techniques perform in larger scale environments. We conducted two studies, each with 30 participants, to investigate how different techniques impact usability and performance for room-scale handheld VR object translations. We compared three translation techniques that are similar to commonly studied techniques in handheld AR: 3DSlide, VirtualGrasp, and Joystick. We also examined the effects of target size, target distance, and user mobility conditions (stationary vs. moving). Results indicated that the Joystick technique, which allowed translation in relation to the user's perspective, was the fastest and most preferred, without difference in precision. Our findings provide insights for designing room-scale handheld VR systems, with potential implications for mixed reality systems involving handheld devices.

To support efficient, balanced news consumption, merging articles from diverse sources into one, potentially through crowdsourcing, could alleviate some hurdles. However, the merging process could also impact annotators’ attitudes towards the content. To test this theory, we propose comparative news annotation; that is, annotating similarities and differences between a pair of articles. By developing and deploying NewsComp—a prototype system—we conducted a between-subjects experiment (N = 109) to examine how users’ annotations compare to experts’, and how comparative annotation affects users’ perceptions of article credibility and quality. We found that comparative annotation can marginally impact users’ credibility perceptions in certain cases; it did not impact perceptions of quality. While users’ annotations were not on par with experts’, they showed greater precision in finding similarities than in identifying disparate important statements. The comparison process also led users to notice differences in information placement and depth, degree of factuality/opinion, and empathetic/inflammatory language use. We discuss implications for the design of future comparative annotation tasks.

Collaborative writing is prevalent, yet is rife with issues of discomfort and miscommunication among others despite access to limitless text. Editing, one key activity in collaborative writing, is prone to such issues, and providing rationales could be a promising solution. To understand the efficacy of rationales in this context, we conducted an online experiment pairing 40 participants to co-write two essays on Google Docs—one without rationales (control) and another with rationales (treatment)—followed by post-experiment surveys (N=40) and interviews (N=11) with participants who received edits with and without rationales. Despite no significant differences between conditions in survey results, interviews revealed that most people preferred collaborating with those who provided rationales and perceived them more favorably. All interviewees deemed rationales important in collaborative writing and felt the pros outweighed the cons. We contribute design recommendations with illustrative examples for effective collaborative writing.

The construction industry is a new avenue for big data and data science with sensors and cyber-physical systems deployed in the field. Construction students need to develop computational thinking skills to help make sense of this data, but existing data science environments designed with textual programming languages create a significant barrier to entry. To bridge this gap, we introduce Octave, an end-user programming environment designed to help non-expert programmers analyze spatiotemporal data (e.g., as gathered by a GPS sensor) in an interactive graphical user interface. To aid exploration and understanding, Octave's design incorporates a high degree of liveness, highlighting the interconnection between data, computation, and visualization. We share the underlying design principles behind Octave and details about the system design and implementation. To evaluate Octave, we conducted a usability study with students studying construction. The results show that non-programmer construction students were able to learn Octave easily and were able to effectively use it to solve domain-specific problems from construction education. The participants appreciated Octave's liveness and felt they could easily connect it to real-life problems in their field. Our work informs the design of future accessible end-user programming environments for data analysis targeting non-experts.

The written word is an asynchronous form of communication through which static texts are exchanged. However, the act of writing is a dynamic cognitive process in which evolving ideas are organized into a coherent narrative. This process is not shared with readers because they only see the final product. With the emerging culture of live streaming and sharing creative practice with online audiences, writing replays can be a new expressive medium and content for writers and another way to engage with reading for the audience. In this paper, we explore the benefits of watching a real-time writing replay. We recorded the writing processes of professional writers (n = 13) and interviewed them about their and others’ replays. These writers found replays engaging. Replays facilitated self-reflection and helped writers empathize with each other. In addition, we conducted an online survey (n = 78) to compare readers’ comprehension and perception of writing replays with those of traditional text. We found that writing replays enhanced user engagement and the perceived quality of the text for some writing styles. Participants from both studies considered using writing replays regularly.

To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users’ feeds, limiting users’ exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others’. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube—a browser extension for YouTube that displays strangers’ personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers’ YouTube recommendations. We conducted a 10-day-long user study (n = 41) followed by a post-study interview (n = 11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others’, recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers.

Modern knowledge workers typically need to use multiple resources, such as documents, web pages, and applications, at the same time. This complexity in their computing environments forces workers to restore various resources in the course of their work. However, conventional curation methods like bookmarks, recent document histories, and file systems place limitations on effective retrieval. Such features typically work only for resources of one type within one application, ignoring the interdependency between resources needed for a single task. In addition, text-based handles do not provide rich cues for users to recognize their associated resources. Hence, the need to locate and reopen relevant resources can significantly hinder knowledge workers’ productivity. To address these issues, we designed and developed Scrapbook, a novel application for digital resource curation across applications that uses screenshot-based bookmarks. Scrapbook extracts and stores all the metadata (URL, file location, and application name) of windows visible in a captured screenshot to facilitate restoring them later. A week-long field study indicated that screenshot-based bookmarks helped participants curate digital resources. Additionally, participants reported that multimodal—visual and textual—data helped them recall past computer activities and reconstruct working contexts efficiently.

User Interface (UI) mockups are commonly used as shared context during interface development collaboration. In practice, UI designers often use screenshots and sketches to create mockups of desired UI behaviors for communication. However, in the later stages of UI development, interfaces can be arbitrarily complex, making it labor-intensive to sketch, and static screenshots are limited in the types of interactive and dynamic behaviors they can express. We introduce CoCapture, a system that allows designers to easily create UI behavior mockups on existing web interfaces by demonstrating and remixing, and to accurately describe their requests to helpers by referencing the resulting mockups using hypertext. We showed that participants could more accurately describe UI behaviors with CoCapture than with existing sketch and communication tools and that the resulting descriptions were clear and easy to follow. Our approach can help teams develop UIs efficiently by bridging communication gaps with more accurate visual context.

Struggling to curb misinformation, social media platforms are experimenting with design interventions to enhance consumption of credible news on their platforms. Some of these interventions, such as the use of warning messages, are examples of nudges---a choice-preserving technique to steer behavior. Despite their application, we do not know whether nudges could steer people into making conscious news credibility judgments online and if they do, under what constraints. To answer, we combine nudge techniques with heuristic based information processing to design NudgeCred--a browser extension for Twitter. NudgeCred directs users' attention to two design cues: authority of a source and other users' collective opinion on a report by activating three design nudges---Reliable, Questionable, and Unreliable, each denoting particular levels of credibility for news tweets. In a controlled experiment, we found that NudgeCred significantly helped users (n=430) distinguish news tweets' credibility, unrestricted by three behavioral confounds---political ideology, political cynicism, and media skepticism. A five-day field deployment with twelve participants revealed that NudgeCred improved their recognition of news items and attention towards all of our nudges, particularly towards Questionable. Among other considerations, participants proposed that designers should incorporate heuristics that users' would trust. Our work informs nudge-based system design approaches for online media.

As news organizations embrace transparency practices on their websites to distinguish themselves from those spreading misinformation, HCI designers have the opportunity to help them effectively utilize the ideals of transparency to build trust. How can we utilize transparency to promote trust in news? We examine this question through a qualitative lens by interviewing journalists and news consumers---the two stakeholders in a news system. We designed a scenario to demonstrate transparency features using two fundamental news attributes that convey the trustworthiness of a news article: source and message. In the interviews, our news consumers expressed the idea that news transparency could be best shown by providing indicators of objectivity in two areas (news selection and framing) and by providing indicators of evidence in four areas (presence of source materials, anonymous sourcing, verification, and corrections upon erroneous reporting). While our journalists agreed with news consumers' suggestions of using evidence indicators, they also suggested additional transparency indicators in areas such as the news reporting process and personal/organizational conflicts of interest. Prompted by our scenario, participants offered new design considerations for building trustworthy news platforms, such as designing for easy comprehension, presenting appropriate details in news articles (e.g., showing the number and nature of corrections made to an article), and comparing attributes across news organizations to highlight diverging practices. Comparing the responses from our two stakeholder groups reveals conflicting suggestions with trade-offs between them. Our study has implications for HCI designers in building trustworthy news systems.

Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to the project's users. This article asks to what degree Wikipedia articles in three languages - Hindi, Urdu, and English - achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles to gauge popularity of the pages in our corpus, and responsiveness of editors to breaking news events and problematic edits. Additionally, we interviewed editors from all three language editions to learn about differences in editing processes and motivations, and we compared the text of the articles across languages as they appeared shortly after the revocation of Article 370. Across languages, we saw discrepancies in article tone, organization, and the information presented, as well as differences in how editors collaborate and communicate with one another. Nevertheless, in Hindi and Urdu, as well as English, editors predominantly try to adhere to the principle of neutral point of view (NPOV), and for the most part, the editors quash attempts by other editors to push political agendas.

Online tools enable users to co-create artifacts remotely. However, creative collaborations can also occur for the social process of collaboration itself, for which measures of success and engagement expectations can be more ambiguous, and individuals' dedication and social dynamics more important. Co-curating music in collaborative playlists (CPs) is one example of creative collaboration that encompasses both roles, and can therefore have more subtleties within its interactions. We conducted two studies using online surveys to understand perceived comfort with and hesitation toward the social dynamics embedded in CPs. Differences in collaborators' ownership perceptions toward CPs and their comfort in interacting with these CPs emerged. We also found a varying desire for situated communication, dependent upon the action taken and perceived ownership (of both a CP and the songs contained), with more users expecting greater comfort when a communication channel exists. From these results, we present four design considerations for more positive and engaging experiences in creative online co-curation.

Computers are used for various purposes and frequent context switch is inevitable. In this setting, retrieving the documents, files, and web pages that have been used for a task can be a challenge. While modern applications provide a history of recent documents for users to resume work, this is not sufficient to retrieve all the digital resources relevant to a given primary document. The histories currently available - file names, web page titles, or URLs - does not take into account the complex dependencies that exist among resources across applications. To address this problem, we tested the idea of using a visual history of a computer screen to retrieve digital resources within a few days through the development of ScreenTrack. ScreenTrack is software that captures screenshots of a computer at regular intervals. It then generates a time-lapse video from the captured screenshots and lets users retrieve a recently opened document or web page from a screenshot that they recognize from its visuals. Through a controlled user study, it was found that participants were able to retrieve requested information more quickly with ScreenTrack than under the control condition. A follow-up study showed that the participants used ScreenTrack to retrieve previously used resources, in order to resume interrupted tasks.

Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to its users. This article asks to what degree Wikipedia articles in three languages --- Hindi, Urdu, and English --- achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles. Additionally, we interviewed editors from all three Wikipedias to learn differences in editing processes and motivations. While activity on South Asian language editions of Wikipedia is growing, at the time of writing, the Hindi and Urdu editions are still in their nascency. In Hindi and Urdu, as well as English, editors predominantly adhere to the principle of neutral point of view (NPOV), and these editors quash attempts by other editors to push political agendas.

Programming support methods, like discussion fo-rums and office hours, are important in CS education, but difficult to scale. In this paper, we introduce EdCode, a system that allows students to seek remote instructional support within their IDE in a way that resembles in-person support. It also allows instructors to provide contextualized responses by referencing students' code, and curate and publish their answers for an entire class by selecting only the relevant part of the code referenced, thereby helping to avoid plagiarism. We evaluated EdCode with a series of usability studies and identified benefits and challenges for its use in programming courses. Students found that the perceived quality of support from EdCode was comparable to that of support from in-person office hours, and both students and instructors found publishing and viewing other students' answers helpful.

Facilitating audience participation in a music performance brings with it challenges in involving non-expert users in large-scale collaboration. A musical piece needs to be created live, over a short period of time, with limited communication channels. To address this challenge, we propose to incorporate social interaction through mobile music instruments that the audience is given to play with, and examine how this feature sustains and affects the audience involvement. We test this idea with an audience participation music system, Crowd in C. We realized a participation-based musical performance with the system and validated our approach by analyzing the interaction traces of the audience at a performance. The result indicates that the audience members were actively engaged throughout the performance, with multiple layers of social interaction available in the system. We also present how the social interactivity among the audience shaped their interaction in the music making process.

We introduce an audience participation system for a music piece, Crowd in C. A distributed musical instrument is implemented entirely on a web browser for an audience to easily participate in music making with their smartphones and to generate sound on their palms. This web-based instrument is designed to encourage an audience to play music together and to interact with other audience members. Each participant composes a short musical tune that will be a musical profile of oneself. Once a profile is submitted, they can browse other people's profiles and play a pattern they like in pairs as if one improvises with another musician. The use of musical profiles is a metaphor for online dating websites and the network created by the instrument mimics such social interaction where a user browses profiles, likes someone, and mingle with other online users.

Web developers use responsive web design to create user interfaces that can adapt to many form factors. To define responsive pages, developers must use Cascading Style Sheets (CSs) or libraries and tools built on top of it. CSS provides high customizability, but requires significant experience. As a result, non-programmers and novice programmers generally lack a means of easily building custom responsive web pages. In this paper, we present a new approach that allows users to create custom responsive user interfaces without writing program code. We demonstrate the feasibility and effectiveness of the approach through a new system we built, named Expresso. With Expresso, users define “keyframes” - examples of how their VI should look for particular viewport sizes - by simply directly manipulating elements in a WYSIWYG editor. Expresso uses these keyframes to infer rules about the responsive behavior of elements, and automatically renders the appropriate css for a given viewport size. To allow users to create the desired appearance of their page at all viewport sizes, Expresso lets users define either a “smooth” or “jump” transition between adjacent keyframes. We conduct a user study and show that participants are able to effectively use Expresso to build realistic responsive interfaces.

Real-time collaboration between a requester and crowd workers expands the scope of tasks that crowdsourcing can be used for by letting requesters and crowd workers interactively create various artifacts (e.g., a sketch prototype, writing, or program code). In such systems, it is increasingly common to allow requesters to verbally describe their requests, receive responses from workers, and provide immediate and continuous feedback to enhance the overall outcome of the two groups' real-time collaboration. This work is motivated by the lack of a deep understanding of the challenges that end users of such systems face in their communication with workers and the need of design implications that can address such challenges for other similar systems. In this paper, we investigate how requesters verbally communicate and collaborate with crowd workers to solve a complex task. Using a crowd-powered UI design tool, we conducted a qualitative user study to explore how requesters with varying expertise communicate and collaborate with crowd workers. Our work also identifies the unique challenges that collaborative crowdsourcing systems pose: potential expertise differences between requesters and crowd workers, the asymmetry of two-way communication (e.g., speech versus text), and the shared artifact's concurrent modification by two disparate groups. Finally, we make design recommendations that can inform the design of future real-time collaboration processes in crowdsourcing systems.

Software developers rely on support from a variety of resources---including other developers---but the coordination cost of finding another developer with relevant experience, explaining the context of the problem, composing a specific help request, and providing access to relevant code is prohibitively high for all but the largest of tasks. Existing technologies for synchronous communication (e.g. voice chat) have high scheduling costs, and asynchronous communication tools (e.g. forums) require developers to carefully describe their code context to yield useful responses. This paper introduces Codeon, a system that enables more effective task hand-off between end-user developers and remote helpers by allowing asynchronous responses to on-demand requests. With Codeon, developers can request help by speaking their requests aloud within the context of their IDE. Codeon automatically captures the relevant code context and allows remote helpers to respond with high-level descriptions, code annotations, code snippets, and natural language explanations. Developers can then immediately view and integrate these responses into their code. In this paper, we describe Codeon, the studies that guided its design, and our evaluation that its effectiveness as a support tool. In our evaluation, developers using Codeon completed nearly twice as many tasks as those who used state-of-the-art synchronous video and code sharing tools, by reducing the coordination costs of seeking assistance from other developers.

The massive amount of digital music data available necessitates automated methods for processing, classifying and organizing large volumes of songs. As music discovery and interactive music applications become commonplace, the ability to synchronize lyric text information with an audio recording has gained interest. This paper presents an approach for lyric-audio alignment by comparing synthesized speech with a vocal track removed from an instrument mixture using source separation. We take a hierarchical approach to solve the problem, assuming a set of paragraph-music segment pairs is given and focus on within-segment lyric alignment at the word level. A synthesized speech signal is generated to reflect the properties of the music signal by controlling the speech rate and gender. Dynamic time warping finds the shortest path between the synthesized speech and separated vocal. The resulting path is used to calculate the timestamps of words in the original signal. The system results in approximately half a second of misalignment error on average. Finally, we discuss the challenges and suggest improvements to the method.

Low-fidelity prototyping at the early stages of user interface (UI) design can help designers and system builders quickly explore their ideas. However, interactive behaviors in such prototypes are often replaced by textual descriptions because it usually takes even professionals hours or days to create animated interactive elements due to the complexity of creating them. In this paper, we introduce SketchExpress, a crowd-powered prototyping tool that enables crowd workers to create reusable interactive behaviors easily and accurately. With the system, a requester-designers or end-users-describes aloud how an interface should behave and crowd workers make the sketched prototype interactive within minutes using a demonstrate-remix-replay approach. These behaviors are manually demonstrated, refined using remix functions, and then can be replayed later. The recorded behaviors persist for future reuse to help users communicate with the animated prototype. We conducted a study with crowd workers recruited from Mechanical Turk, which demonstrated that workers could create animations using SketchExpress in 2.9 minutes on average with 27% gain in the quality of animations compared to the baseline condition of manual demonstration.

This statement describes LOLC, a text-based collaborative music improvisation environment for laptop ensemble developed at Georgia Tech and presented in performance by the authors at the Creativity and Cognition conference.

This paper discusses LOLC, a text-based collaborative music improvisation system for laptop ensemble developed by the authors. The paper evaluates LOLC in the context of a recent performance by professional classical musicians with minimal computer experience. Using qualitative data from interviews with the performers and quantitative data from server logs, the paper considers the degree to which LOLC facilitated collaborative improvisation among the musicians and the degree to which LOLC was accessible to non-programmers to learn and perform.